What are the very basic algorithms that every Computer Science student must be aware of?

The benefits of most algorithms will be hard to explain to someone who has not yet been introduced to them, but I tried my best of giving a hint for their role. 1. Naive Bayes. Classification is a natural first step to machine learning and the Naive Bayes classifier combines that with the trending concept of Bayesian inference. Despite its simplicity, can address key problems like spam classification. 2. Map-Reduce. A simple and visual way of leveraging multiple computers to solve a single task. More on its different uses here: MapReduce Patterns, Algorithms, and Use Cases. All the best.

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Going to present algorithms that carry over to engineering, not just research and TopCoder. Another goal was for these algorithms to be useful and widely applicable hammers in their own right, not just as an idea. The benefits of most algorithms will be hard to explain to someone who has not yet been introduced to them, but I tried my best of giving a hint for their role. 1. Naive Bayes. Classification is a natural first step to machine learning and the Naive Bayes classifier combines that with the trending concept of Bayesian inference. Despite its simplicity, can address key problems like spam classification. 2. Map-Reduce. A simple and visual way of leveraging multiple computers to solve a single task. More on its different uses here: MapReduce Patterns, Algorithms, and Use Cases. 3. Hill Climbing. Introduces optimization over a non-exhaustible domain and leads to a whole array of powerful methods like gradient-descent, simulated annealing. Optimization allows us to do decision making, it's important. 4. Binary search. Introduces logarithmic complexity and how the structure of data can change complexity characteristics of operations.

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Any computer science graduate wishing to succeed in his/her career as a programmer must have understanding of at least one programming language, preferable an object oriented language, data structures and software engineering. Functional domain knowledge in any area is icing on cake. Computer engineers must know following concepts and algorithms: (a) Basics of Data Structures such as Stacks, Queues, Arrays, Linked Lists, etc. (b) Understanding of Sorting Algorithms Insertion Sort, Selection Sort, Merge Sort, Quicksort, Heapsort, Bucket Sort, Radix Sort, etc. (c) Graphs and Graph Algorithms Dijkstras Algorithm, Prims Algorithm, Kruskals Algorithm, Depth-First vs Breadth-First Search, Connected Components, Maximum Flow, etc. (d) Dynamic Programming Knapsack Problem, All-Pairs Shortest Path, etc. (e) Special Algorithms such as Greedy Algorithms, Divide and Conquer, Dynamic Programming, Randomized Algorithms and Backtracking Algorithms, etc.

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Coming from an intensive programming contest background, having read Introduction to Algorithms, etc., but then moving on to doing real-life projects, I find that some of the more interesting and powerful algorithms in a problem-solving setting find little use in the industry. I'm thus going to present algorithms that carry over to engineering, not just research and TopCoder. Another goal was for these algorithms to be useful and widely applicable hammers in their own right, not just as an idea. The benefits of most algorithms will be hard to explain to someone who has not yet been introduced to them, but I tried my best of giving a hint for their role. 1.Naive Bayes. Classification is a natural first step to machine learning and the Naive Bayes classifier combines that with the trending concept of Bayesian inference. Despite its simplicity, can address key problems like spam classification. 2.Map-Reduce. A simple and visual way of leveraging multiple computers to solve a single task. More on its different uses here: MapReduce Patterns, Algorithms, and Use Cases. 3.Hill Climbing. Introduces optimization over a non-exhaustible domain and leads to a whole array of powerful methods like gradient-descent, simulated annealing. Optimization allows us to do decision making, it's important. 4.Binary search. Introduces logarithmic complexity and how the structure of data can change complexity characteristics of operations. 5.Exponentiation (powers) by halving. A great example of a recursive attack. You would be at surprised how many problems can be described as exponentiation (for an associative operation) of something: a number, a matrix and more. Again it's magical that associativity of an operation allows you to turn a linear factor into a logarithmic one. 6.Floyd-Warshall. Graphs as matrices, shortest paths, negative cycles, dynamic programming in five lines of code.

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